





'''simple docstring'''


import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


lowerCAmelCase   :	Union[str, Any]   				=getLogger(__name__)
def 				UpperCAmelCase_    (		__lowerCamelCase   :   Union[str, Any]      ,__lowerCamelCase   :   Dict      ,__lowerCamelCase   :   int      ,__lowerCamelCase   :   Union[str, Any] = 8      ,__lowerCamelCase   :   Union[str, Any] = 10_24      ,__lowerCamelCase   :   Optional[Any]="val"      ,__lowerCamelCase   :   int=None      ,__lowerCamelCase   :   Dict=False      ,__lowerCamelCase   :   int="summarization"      ,__lowerCamelCase   :   Tuple=None      ,__lowerCamelCase   :   str=1      ,__lowerCamelCase   :   Dict = None      ,__lowerCamelCase   :   Tuple=""      ,**__lowerCamelCase   :   Union[str, Any]      ,):
   lowercase_     :Any            =					str(a__					)
   assert local_rank is not None
   torch.distributed.init_process_group(backend="nccl"      ,rank=a__					)

   lowercase_     :Tuple            =					Path(a__					)
   lowercase_     :Optional[Any]            =					save_dir.joinpath(F'rank_{local_rank}_output.json'					)
   torch.cuda.set_device(a__					)
   lowercase_     :List[str]            =					AutoModelForSeqaSeqLM.from_pretrained(a__					).cuda()
   if fpaa:
      lowercase_     :Optional[Any]            =					model.half()
   # determine if we need to increase num_beams
   use_task_specific_params(a__      ,a__					)  # update config with task specific params
   lowercase_     :List[str]            =					generate_kwargs.pop("num_beams"      ,model.config.num_beams					)  # AttributeError risk?
   if num_return_sequences > num_beams:
      lowercase_     :Tuple            =					num_return_sequences

   lowercase_     :List[Any]            =					AutoTokenizer.from_pretrained(a__					)
   logger.info(F'Inferred tokenizer type: {tokenizer.__class__}'					)  # if this is wrong, check config.model_type.

   if max_source_length is None:
      lowercase_     :str            =					tokenizer.model_max_length
   if prefix is None:
      lowercase_     :str            =					prefix or getattr(model.config      ,"prefix"      ,""					) or """"""
   lowercase_     :Optional[int]            =					SeqaSeqDataset(
       a__      ,a__      ,a__      ,max_target_length=10_24      ,type_path=a__      ,n_obs=a__      ,prefix=a__      ,**a__      ,)
   # I set shuffle=True for a more accurate progress bar.
   # If all the longest samples are first, the prog bar estimate is too high at the beginning.
   lowercase_     :Optional[int]            =					ds.make_sortish_sampler(a__      ,distributed=a__      ,add_extra_examples=a__      ,shuffle=a__					)
   lowercase_     :Dict            =					DataLoader(a__      ,sampler=a__      ,batch_size=a__      ,collate_fn=ds.collate_fn					)
   lowercase_     :Dict            =					[]
   for batch in tqdm(a__					):
      lowercase_     :Any            =					model.generate(
          input_ids=batch["input_ids"].to(model.device					)      ,attention_mask=batch["attention_mask"].to(model.device					)      ,num_return_sequences=a__      ,num_beams=a__      ,**a__      ,)
      lowercase_     :List[str]            =					tokenizer.batch_decode(a__      ,skip_special_tokens=a__      ,clean_up_tokenization_spaces=a__					)
      lowercase_     :Union[str, Any]            =					batch["""ids"""]
      if num_return_sequences > 1:
         lowercase_     :int            =					chunks(a__      ,a__					)  # batch size chunks, each of size num_return_seq
      for i, pred in enumerate(a__					):
         results.append({"pred": pred, "id": ids[i].item()}					)
   save_json(a__      ,a__					)
   return results, sampler.num_replicas
def 				UpperCAmelCase_    (		):
   lowercase_     :int            =					argparse.ArgumentParser(
       epilog="Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate"					)
   parser.add_argument("--data_dir"      ,type=a__      ,help="like cnn_dm/test.source"					)
   parser.add_argument(
       "--model_name"      ,type=a__      ,help="like facebook/bart-large-cnn,t5-base, etc."      ,default="sshleifer/distilbart-xsum-12-3"      ,)
   parser.add_argument("--save_dir"      ,type=a__      ,help="where to save"      ,default="tmp_gen"					)
   parser.add_argument("--max_source_length"      ,type=a__      ,default=a__					)
   parser.add_argument(
       "--type_path"      ,type=a__      ,default="test"      ,help="which subset to evaluate typically train/val/test"					)
   parser.add_argument("--task"      ,type=a__      ,default="summarization"      ,help="used for task_specific_params + metrics"					)
   parser.add_argument("--bs"      ,type=a__      ,default=8      ,required=a__      ,help="batch size"					)
   parser.add_argument(
       "--local_rank"      ,type=a__      ,default=-1      ,required=a__      ,help="should be passed by distributed.launch"					)

   parser.add_argument(
       "--n_obs"      ,type=a__      ,default=a__      ,required=a__      ,help="How many observations. Defaults to all."					)
   parser.add_argument(
       "--num_return_sequences"      ,type=a__      ,default=1      ,required=a__      ,help="How many sequences to return"					)
   parser.add_argument(
       "--sync_timeout"      ,type=a__      ,default=6_00      ,required=a__      ,help="How long should master process wait for other processes to finish."      ,)
   parser.add_argument("--src_lang"      ,type=a__      ,default=a__      ,required=a__					)
   parser.add_argument("--tgt_lang"      ,type=a__      ,default=a__      ,required=a__					)
   parser.add_argument(
       "--prefix"      ,type=a__      ,required=a__      ,default=a__      ,help="will be added to the begininng of src examples"					)
   parser.add_argument("--fp16"      ,action="store_true"					)
   parser.add_argument("--debug"      ,action="store_true"					)
   lowercase_     :Union[str, Any]            =					time.time()
   lowercase_     :Optional[Any]            =					parser.parse_known_args()
   lowercase_     :int            =					parse_numeric_n_bool_cl_kwargs(a__					)
   if generate_kwargs and args.local_rank <= 0:
      print(F'parsed the following generate kwargs: {generate_kwargs}'					)
   lowercase_     :str            =					Path(args.save_dir + "_tmp"					)
   Path(a__					).mkdir(exist_ok=a__					)  # this handles locking.
   lowercase_     :str            =					list(json_save_dir.glob("rank_*.json"					)					)
   if intermediate_files:
      raise ValueError(F'Found files at {json_save_dir} please move or remove them.'					)
      # In theory, a node could finish and save before another node hits this. If this happens, we can address later.
   lowercase_     :Dict            =					{}
   if args.src_lang is not None:
      lowercase_     :Dict            =					args.src_lang
   if args.tgt_lang is not None:
      lowercase_     :str            =					args.tgt_lang

   Path(args.save_dir					).mkdir(exist_ok=a__					)
   lowercase_     :Tuple            =					eval_data_dir(
       args.data_dir      ,a__      ,args.model_name      ,type_path=args.type_path      ,bs=args.bs      ,fpaa=args.fpaa      ,task=args.task      ,local_rank=args.local_rank      ,n_obs=args.n_obs      ,max_source_length=args.max_source_length      ,num_return_sequences=args.num_return_sequences      ,prefix=args.prefix      ,dataset_kwargs=a__      ,**a__      ,)

   if args.local_rank <= 0:
      lowercase_     :List[str]            =					Path(args.save_dir					)
      save_dir.mkdir(exist_ok=a__					)
      lowercase_     :Tuple            =					gather_results_from_each_node(a__      ,a__      ,args.sync_timeout					)
      lowercase_     :str            =					combine_partial_results(a__					)
      if args.num_return_sequences > 1:
         lowercase_     :str            =					save_dir.joinpath("pseudolabel_results.json"					)
         print(F'Saving aggregated results at {save_path}, intermediate in {json_save_dir}/'					)
         save_json(a__      ,a__					)
         return
      lowercase_     :Any            =					Path(args.data_dir					).joinpath(args.type_path + ".target"					)
      with open(a__					) as f:
         lowercase_     :Optional[int]            =					[x.rstrip() for x in f.readlines()][: len(a__					)]

      # Calculate metrics, save metrics,  and save _generations.txt
      lowercase_     :Any            =					"""translation""" in args.task
      lowercase_     :Optional[int]            =					calculate_bleu if calc_bleu else calculate_rouge
      lowercase_     :Union[str, Any]            =					"""bleu""" if calc_bleu else """rouge"""
      lowercase_     :Dict            =					score_fn(a__      ,a__					)
      lowercase_     :Optional[Any]            =					len(a__					)
      lowercase_     :Tuple            =					time.time() - start_time
      lowercase_     :Optional[int]            =					round(runtime / metrics["n_obs"]      ,4					)
      lowercase_     :List[str]            =					num_replicas
      # TODO(@stas00): add whatever metadata to metrics
      lowercase_     :int            =					save_dir.joinpath(F'{args.type_path}_{metric_name}.json'					)
      save_json(a__      ,a__      ,indent=a__					)
      print(a__					)
      write_txt_file(a__      ,save_dir.joinpath(F'{args.type_path}_generations.txt'					)					)
      if args.debug:
         write_txt_file(a__      ,save_dir.joinpath(F'{args.type_path}.target'					)					)
      else:
         shutil.rmtree(a__					)
def 				UpperCAmelCase_    (		__lowerCamelCase   :   Union[str, Any]					):
   lowercase_     :Union[str, Any]            =					[]
   for partial_result in partial_results:
      records.extend(a__					)
   lowercase_     :Any            =					sorted(a__      ,key=lambda __lowerCamelCase					: x["id"]					)
   lowercase_     :Optional[int]            =					[x["""pred"""] for x in records]
   return preds
def 				UpperCAmelCase_    (		__lowerCamelCase   :   List[str]      ,__lowerCamelCase   :   Dict      ,__lowerCamelCase   :   Optional[int]					):
   lowercase_     :List[Any]            =					time.time()
   logger.info("waiting for all nodes to finish"					)
   lowercase_     :Optional[int]            =					None
   while (time.time() - start_wait) < timeout:
      lowercase_     :Union[str, Any]            =					list(save_dir.glob("rank_*.json"					)					)
      if len(a__					) < num_replicas:
         continue
      try:
         # make sure all json files are fully saved
         lowercase_     :Tuple            =					lmap(a__      ,a__					)
         return json_data
      except JSONDecodeError:
         continue
   else:
      raise TimeoutError("Rank 0 gave up on waiting for other processes"					)
   # Unreachable


if __name__ == "__main__":
 # Usage for MT:
 run_generate()





from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch



@require_torch
@require_tf
class        A__(a_							):
						"""simple docstring"""






						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						List[Any] 							= SMALL_MODEL_IDENTIFIER
									a_						:						Optional[int] 							= """pt"""
									a_						:						Union[str, Any] 							= """tf"""





						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> str:
									a_						:						Union[str, Any] 							= AutoModel.from_pretrained(self.test_model    )
									model_pt.save_pretrained(_lowercase    )





						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> List[str]:
									a_						:						int 							= TFAutoModel.from_pretrained(self.test_model  ,      from_pt=_lowercase    )
									model_tf.save_pretrained(_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									a_						:						str 							= """mock_framework"""

									# Framework provided - return whatever the user provides
									a_						:						Optional[int] 							= FeaturesManager.determine_framework(self.test_model  ,      _lowercase    )
									self.assertEqual(_lowercase  ,      _lowercase    )

									# Local checkpoint and framework provided - return provided framework
									# PyTorch checkpoint
									with TemporaryDirectory() as local_pt_ckpt:
												self._setup_pt_ckpt(_lowercase    )
												a_						:						Optional[int] 							= FeaturesManager.determine_framework(_lowercase  ,      _lowercase    )
												self.assertEqual(_lowercase  ,      _lowercase    )

									# TensorFlow checkpoint
									with TemporaryDirectory() as local_tf_ckpt:
												self._setup_tf_ckpt(_lowercase    )
												a_						:						List[str] 							= FeaturesManager.determine_framework(_lowercase  ,      _lowercase    )
												self.assertEqual(_lowercase  ,      _lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Union[str, Any]:
									# PyTorch checkpoint
									with TemporaryDirectory() as local_pt_ckpt:
												self._setup_pt_ckpt(_lowercase    )
												a_						:						Union[str, Any] 							= FeaturesManager.determine_framework(_lowercase    )
												self.assertEqual(_lowercase  ,      self.framework_pt    )

									# TensorFlow checkpoint
									with TemporaryDirectory() as local_tf_ckpt:
												self._setup_tf_ckpt(_lowercase    )
												a_						:						Union[str, Any] 							= FeaturesManager.determine_framework(_lowercase    )
												self.assertEqual(_lowercase  ,      self.framework_tf    )

									# Invalid local checkpoint
									with TemporaryDirectory() as local_invalid_ckpt:
												with self.assertRaises(_lowercase    ):
															a_						:						Union[str, Any] 							= FeaturesManager.determine_framework(_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									a_						:						int 							= MagicMock(return_value=_lowercase    )
									with patch("""transformers.onnx.features.is_tf_available"""  ,      _lowercase    ):
												a_						:						Union[str, Any] 							= FeaturesManager.determine_framework(self.test_model    )
												self.assertEqual(_lowercase  ,      self.framework_pt    )

									# PyTorch not in environment -> use TensorFlow
									a_						:						Dict 							= MagicMock(return_value=_lowercase    )
									with patch("""transformers.onnx.features.is_torch_available"""  ,      _lowercase    ):
												a_						:						Union[str, Any] 							= FeaturesManager.determine_framework(self.test_model    )
												self.assertEqual(_lowercase  ,      self.framework_tf    )

									# Both in environment -> use PyTorch
									a_						:						Optional[Any] 							= MagicMock(return_value=_lowercase    )
									a_						:						List[Any] 							= MagicMock(return_value=_lowercase    )
									with patch("""transformers.onnx.features.is_tf_available"""  ,      _lowercase    ), patch(
									    """transformers.onnx.features.is_torch_available"""  ,      _lowercase    ):
												a_						:						Any 							= FeaturesManager.determine_framework(self.test_model    )
												self.assertEqual(_lowercase  ,      self.framework_pt    )

									# Both not in environment -> raise error
									a_						:						List[str] 							= MagicMock(return_value=_lowercase    )
									a_						:						Optional[Any] 							= MagicMock(return_value=_lowercase    )
									with patch("""transformers.onnx.features.is_tf_available"""  ,      _lowercase    ), patch(
									    """transformers.onnx.features.is_torch_available"""  ,      _lowercase    ):
												with self.assertRaises(_lowercase    ):
															a_						:						Dict 							= FeaturesManager.determine_framework(self.test_model    )


